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Forecasting Time Series Data with Prophet

You're reading from   Forecasting Time Series Data with Prophet Build, improve, and optimize time series forecasting models using Meta's advanced forecasting tool

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Product type Paperback
Published in Mar 2023
Publisher Packt
ISBN-13 9781837630417
Length 282 pages
Edition 2nd Edition
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Author (1):
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Greg Rafferty Greg Rafferty
Author Profile Icon Greg Rafferty
Greg Rafferty
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Getting Started with Prophet
2. Chapter 1: The History and Development of Time Series Forecasting FREE CHAPTER 3. Chapter 2: Getting Started with Prophet 4. Chapter 3: How Prophet Works 5. Part 2: Seasonality, Tuning, and Advanced Features
6. Chapter 4: Handling Non-Daily Data 7. Chapter 5: Working with Seasonality 8. Chapter 6: Forecasting Holiday Effects 9. Chapter 7: Controlling Growth Modes 10. Chapter 8: Influencing Trend Changepoints 11. Chapter 9: Including Additional Regressors 12. Chapter 10: Accounting for Outliers and Special Events 13. Chapter 11: Managing Uncertainty Intervals 14. Part 3: Diagnostics and Evaluation
15. Chapter 12: Performing Cross-Validation 16. Chapter 13: Evaluating Performance Metrics 17. Chapter 14: Productionalizing Prophet 18. Index 19. Other Books You May Enjoy

Applying linear growth

All the models we built in the previous chapters had the default growth mode, linear. This means that the trend consists of a straight, sloped line, or potentially, a few straight, sloped lines connected at changepoints – a case we will explore in Chapter 8, Influencing Trend Changepoints. For now, though, let’s load up our Divvy data again and focus on the growth.

We’re going to import pandas, matplotlib, and Prophet again, but this time, we’ll also import a new function from Prophet’s plot package, add_changepoints_to_plot, as follows:

import pandas as pd
import matplotlib.pyplot as plt
from prophet import Prophet
from prophet.plot import add_changepoints_to_plot

This new function will allow us to easily plot our trend line directly on our forecast plot.

As we’ve done previously, let’s open the Divvy data and load it into our training DataFrame:

df = pd.read_csv('divvy_daily.csv')
df...
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